Measuring the environmental maturity of the supply chain finance: A big data-based multi-criteria perspective

Alidrisi, Hisham Article Measuring the environmental maturity of the supply chain finance: A big data-based multi-criteria perspective Logistics Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Alidrisi, Hisham (2021) : Measuring the environmental maturity of the supply chain finance: A big data-based multi-criteria perspective, Logistics, ISSN 2305-6290, MDPI, Basel, Vol. 5, Iss. 2, pp. 1-24, https://doi.org/10.3390/logistics5020022 This Version is available at: https://hdl.handle.net/10419/310146 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ logistics Article Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective Hisham Alidrisi   Citation: Alidrisi, H. Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective. Logistics 2021,5, 22. https://doi.org/10.3390/ logistics5020022 Academic Editor: Lucila Maria de Souza Campos Received: 10 February 2021 Accepted: 25 March 2021 Published: 13 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia; [email protected] Abstract: This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers. Keywords: GSCM; big data; SCF; fuzzy ANP; fuzzy TOPSIS; ISM 1. Introduction In industrial economics, “Everything is worth what its purchaser will pay for it” (Publilius Syrus, 1st Century B.C., cited in [ 1 ]). Unfortunately, this is not reflected in current green supply chain management (GSCM) practices. Indeed, the cost of GSCM implementation is greater than the expected return [ 2 , 3 ]. The main reason for this is the cost of changing practices, including those surrounding human resources [ 3 ], and adopting new green systems [ 4 ]. Consequently, it is not surprising that financial barriers represent one of the main obstacles to GSCM implementation [2,3,5,6]. It must be noted that the costs of being green are associated with not only environmental elements but also organizational functions [ 7 ]. Hervani et al. [ 5 ] explained this viewpoint through their equation-based definition of GSCM: GSCM = [Green Purchasing (GP)] + [Green Manufacturing (GM)/Materials Management (MM)] + [Green Distribution (GD)/Marketing] + [Reverse Logistics]. Specifically, linking environmental-related strategic purchasing activities to supply chain management (SCM) practices facilitates GSCM implementation [ 8 ]. This, in turn, creates new dimensions for sophisticated networks of buyers and sellers in various industries [ 5 , 9 ]. This complex situation for supply chain stakeholders highlights the need for the implementation of supply chain finance (SCF), a solution developed by academicians and practitioners. SCF has been defined as a facilitator of the physical and information flows of the financial products and services provided by financial institutions [ 10 ]. It is the ability to optimize supply chains to enable financial infrastructures and cash flows [ 11 ]. Wuttke et al. [12] defined SCF as cash flow optimization with respect to planning, management, and control to improve material flows. Other studies have offered similar definitions [ 13 – 18 ]. These Logistics 2021,5, 22. https://doi.org/10.3390/logistics5020022 https://www.mdpi.com/journal/logistics Logistics 2021,5, 22 2 of 24 studies suggest that financial, physical, technological, human, and organizational resource flows are key to SCF. Thus, the definitions are centered around the concept of the “flow” (i.e., flow of information, materials, and other resources) along the supply chain. This indicates the need for a focus on the five Vs of big data: value, volume, velocity, variety, and veracity. Moreover, recent industrial economics studies have also emphasized the importance of big data as a vital dimension in such a dynamic era [19–24]. Touboulic et al. [ 25 ] emphasized that the current SC practices are still influenced by the idea that the developing countries represent a collection of suppliers to the well-established firms in the developed countries; accordingly, SCM is in need of being restructured. Indeed, for SCM, restructuring the relationships among the different stakeholders is an issue that has attracted much attention, and consequently, the field of Operations Management (OM) represents an appropriate field for decision-making (DM) and/or multicriteria decisionmaking (MCDM) applications [ 26 ]. For example, the variety of the variables and constraints corresponding to the vehicle routing problem (VRP) results in formulation of different algorithms in order to optimize various logistics problems [ 27 ]. Likewise, in order to decrease the environmental impacts created by the transportation fleets during transfer of goods among different logistics centers, the queuing theory has been employed [ 28 ]. Tundys and Wi´sniewski [ 29 ] investigated various tools and techniques for measuring the performance of GSCM and they clearly stated that future research attempts should focus on developing “friendly” managerial tools in order to assess GSCM practices. Such interactions among environmental aspects and SCM issues create an appropriate environment in which MCDM tools, such as TOPSIS, are utilized in order to solve GSCM issues [ 30 ]. Fuzzy TOPSIS and ELECTRE have recently been applied in order to handle the issue of selecting green suppliers considering green practices [31]. On top of this, with the increasing number of the recently published GSCM research works that highlight the significance of the issue of “resources” in GSCM practices [ 32 , 33 ], and with consideration of the fact that promising technologies such as blockchain, Internet of Things (IoT), Artificial Intelligence (AI), and data analytics represent the most advanced platforms and/or the state-of-the-art technical facilitators for the data exchanging processes among the SCF stakeholders [ 34 ], in which the flow of big data within the interactive environment represents a cornerstone for formulating a trusted and reliable platform [ 35 ], this paper proposes a strategic roadmap to handle the issue of resource allocation among the GSCM practices. Hence, the corresponding complex hypothesis herein imposes a potential for different strategic configurations to be executed as a result of dealing with different sets of resources needed to be utilized by each GSCM practice. Consequently, three research questions can be formulated: Research Question 1 (RQ1): How do the five Vs of big data interact with each other to improve SCF practices? Research Question 2 (RQ2): To what extent are GSCM practice-related data accurate, valuable, big, plentiful, and fast in terms of improving the data flow to facilitate the SCF implementation? Research Question 3 (RQ3): How do we achieve optimal resource mobilization for GSCM practices considering their different contributions to the SCF implementation? This paper used the five Vs of big data as a platform for testing the role of GSCM practices in improving SCF implementation. Considering the fact that MCDM methods facilitate the formulation of different strategies and the creation of evaluation processes, especially in logistics [ 36 ], three MCDM methods were employed. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their contributions to SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was used to evaluate GSCM practices on the basis of these criteria. To improve the implementation of SCF, this study also provides a roadmap for the visualization of the optimum resource mobilization of GSCM practices through interpretive structural modeling (ISM). The rest of the paper is organized as follows: Section 2introduces the eight GSCM practices extracted from the literature. Some recent MCDM relevant applications are also presented at the Logistics 2021,5, 22 3 of 24 end of Section 2. The employed methods and contexts of their application are presented in Section 3. Section 4provides the results of the proposed model. The implications, contributions, and directions for future studies are presented in Section 5. Finally, Section 6 presents the conclusion for this paper. A list of acronyms and their descriptions are presented in Table 1. Table 1. List of acronyms and their descriptions. Acronym Descriptions AHP Analytic Hierarchy Process AI Artificial Intelligence ANP Analytic Network Process BPM Business Process Modeling BRI Basic Readiness Index CUST Customer Relationship Management DM Decision Making ELECTRE Elimination Et Choice Translating Reality EMM Environmental Management Maturity ENVI Environmental Management GD Green Distribution GHRM Green Human Resource Management GM Green Manufacturing GP Green Purchasing GSCM Green Supply Chain Management HRM Human resource management IoT Internet of Things ISM Interpretive Structural Modeling ISO 14001 The International Standard that Specifies Requirements for an Effective Environmental Management System MCDM Multicriteria Decision-Making MM Materials Management OM Operations Management ORGM Organizational Interaction Maturity PROMETHEE Preference Ranking Organization Method for Enrichment Evaluations QUAL Quality Management RQ Research Question RRI Relative Readiness Index SCF Supply Chain Finance SCM Supply Chain Management SMT Strategic Matrix Tool SP&D Smart Process and Design SUPP Supplier Selection TOPM Top Management Commitment TOPSIS Technique For Order Preference By Similarity To Ideal Solution VRP Vehicle Routing Problem 5Vs Five Vs of big data (value, volume, velocity, variety, and veracity) 2. Background Several research studies have identified GSCM practices. For example, Zhu et al. [ 37 ] listed 21 GSCM practices related to five main factors. Kannan et al. [ 38 ] condensed this list into 17 GSCM practices, which can be grouped into eight vital practices. This section presents the most widely accepted GSCM practices (i.e., published papers within the literature). All aspects of the supporting literature are summarized and presented in Table 2. Logistics 2021,5, 22 4 of 24 Table 2. Summarization of the supporting literature. Research Gap/Aspect Supporting Literature Top Management Commitment (TOPM) [37,39–48] Organizational Interaction Maturity (ORGM) [37,49–56] Quality Management (QUAL) [57–67] Environmental Management (ENVI) [68–73] Customer Relationship Management (CUST) [61,63,65,67,74–80] Green Human Resource Management (GHRM) [56,81–88] Supplier Selection (SUPP) [8,38,45,48,89–98] Smart Process and Design (SP&D) [38,99–104] MCDM Applications in GSCM [38,91,105–113] Justifications for the Selected Methods [106,114–122] 2.1. Top Management Commitment Top management commitment (TOPM) is a crucial driver for greening SCM. Top and senior management commitment is required to implement environmental management systems that are in harmony with other green practices and facilitate the monitoring of the organization’s environmental progress [ 39 ]. Management commitment also enhances internal cross-functional collaborations among operational units. This may include empowering employees [ 40 ], implementing effective reward systems, providing training, and promoting teamwork [ 41 ]. Therefore, management commitment is important in environmental initiatives [ 37 , 42 ]. For example, managers must be committed to applying GSCM to purchasing, such as the reuse and recycling potential of purchased products and materials [ 43 – 48 ]. Indeed, studies have emphasized the importance of top management support to the success of organizational initiatives, including those related to GSCM [37]. 2.2. Organizational Interaction Maturity Organizational theory has undergirded the exploration and elucidation of GSCM [ 49 ]. According to resource-based theory, organizational infrastructure consists of a combination of resources and capabilities that create a competitive advantage [ 50 ]. The effective utilization of these resources leads to the successful implementation of competitive strategies, including those related to the environment [ 37 , 51 ]. Therefore, organizational learning enhances the implementation of sophisticated systems, such as GSCM [37,52–56]. 2.3. Quality Management Quality management (QUAL) is the key to organizational performance. It also plays a significant role in organizational environmental practices [ 57 – 59 ], which lead to better GSCM practices [ 60 – 62 ]. Jabbour et al. [ 62 ] investigated the role of QUAL, environmental management maturity (EMM), and other GSCM practices in green performance. Their study of 95 Brazilian companies found that it is essential to organizational EMM, which can improve green performance, especially in purchasing. Other studies have also emphasized the significance of QUAL in improving GSCM practices [63–67]. 2.4. Environmental Management Waste and costs can be efficiently minimized, and better environmental performance can be achieved when leading companies practice GSCM [ 68 ]. Thus, ISO 14001 plays a significant role in enhancing GSCM. In a study on the effects of ISO 14001 certification in Japanese facilities, Arimura et al. [ 69 ] concluded that these standards encouraged GSCM practices. Other studies have drawn similar conclusions [70–73]. 2.5. Customer Relationship Management One of the main elements of supply chain operations is stakeholder collaboration in the development of environmental goals [ 61 , 63 , 74 ]. Thus, customer pressure plays a Logistics 2021,5, 22 5 of 24 significant role in GSCM [ 75 – 77 ]. Thun and Müller [ 76 ] found that GSCM practices in German manufacturing were driven by customer pressure; consequently, good customer relationships were considered as a competitive advantage. Currently, the application of green requirements to the supply chain constitutes a competitiveness criterion for evaluating firm performance [ 77 ]. Several studies have concluded that customer relationship management is key to the implementation of GSCM practices [65,67,78–80]. 2.6. Green Human Resource Management Human resource management (HRM) practices encourage the creation of green organizations [ 81 ]. Cantor et al. [ 82 ] developed a model to investigate the relationship between organizational environmental initiatives and HRM practices. The key features of green training related to green human resource management (GHRM) and GSCM practices in Brazilian organizations have been investigated [ 56 ]. Jabbour and de Sousa Jabbour [ 83 ] observed that GHRM and GSCM play a significant role in creating a sustainable workplace environment; however, further research is needed. Therefore, an assimilation framework for GHRM–GSCM interactions was proposed. Muduli et al. [ 84 ] used ISM to investigate the relationships among GCSM-related behaviors. To address these complex organizational environmental challenges, GHRM must be considered because of its contribution to employee environmental authorizations [ 85 ], a green organizational culture [ 86 , 87 ], and the enhancement of environmental groups [85,88]. 2.7. Supplier Selection Supplier environmental partnerships promote green purchasing, which improves suppliers’ ecological performances [ 8 ]. Supplier environmental partnerships have been categorized as three main types of activities: supplier education, supplier support, and joint ventures [ 48 ]. Thus, the consideration of green criteria in supplier selection (SUPP) has been investigated [38,45,89–92]. Studies have also focused on green supplier selection [93–98]. 2.8. Smart Process and Design Smart process and design (SP&D) considers product eco-design and reverse logistics. Eco-design refers to the consideration of stakeholder needs in the environmental elements of product design and development [ 99 – 101 ], including sustainable packaging [ 102 ]. Reverse logistics describes the production activities related to the three “REs” of raw materials: reduction, reuse, and recycling [ 99 , 103 , 104 ]. SP&D covers a wide range of successful GSCM practices, such as purchasing equipment that produces clean products, selling obsolete stock, selling waste materials, and reducing energy consumption [38]. 2.9. MCDM Applications in GSCM In this regard, several MCDM studies have addressed GSCM. Although many studies noticed that the lack of MCDM applications is still considered as a research gap, specifically in the field of reverse logistics [ 105 ], Vieira et al. [ 106 ] concluded that several MCDM methods have the potential to investigate several issues related to GSCM such as the prioritizing of the reverse logistics barriers. Kechagias et al. [ 107 ] aimed at creating a process reference model and have developed an analytic hierarchy process (AHP)-based Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) model to propose a systemic methodology for selecting the best Business Process Modeling (BPM) tools. They concluded that the proposed model is capable of contributing to improvement of sustainability in the context of SCM. Büyüközkan and Çifçi [ 108 ] highlighted the importance of the environmental dimension in achieving financial success. Accordingly, they developed a fuzzy-based ANP model to evaluate GSCM practices in an evolved Turkish company. Büyüközkan and Çifçi [ 91 ] emphasized the relationship between environmental performance and internal and external factors. Thus, they developed an MCDM model that employed fuzzy ANP and TOPSIS to optimize supplier selection. Wang and Chan [ 109 ] developed a fuzzy TOPSIS model to increase practitioners’ and decision-makers’ under- Logistics 2021,5, 22 6 of 24 standing of the resources that are needed as a consequence of the greening of SCM practices. A fuzzy TOPSIS model was developed to enable a Brazilian company to optimize electronics supplier selection [ 38 ]. ISM has been employed as a research method in many fields, including GSCM [ 110 – 113 ]. For example, Mathiyazhagan et al. [ 111 ] used sophisticated ISM to examine 26 barriers to GSCM in Indian automobile component manufacturing. Further technical explanations of the procedures for performing fuzzy ANP, fuzzy TOPSIS, and ISM are presented in succeeding sections of the paper. 3. Methods and Applications Three MCDM methods were employed: fuzzy ANP, fuzzy TOPSIS, and ISM. The employment of these methods can be justified by looking at the three research questions separately and collectively. RQ1 aims to investigate the interactions among the “five Vs. of big data” to improve SCF practices. Such interactions imply the existence of “dependency” and “feedback” relationships among the “five Vs”. The ANP, in particular, is a best fit tool that can discover, handle, and quantify such relationships [ 114 , 115 ]. This point has also been discussed and validated in many ANP-based research works [ 116 – 118 ]. RQ2 aims to evaluate eight different GSCM practices with respect to the five criteria (i.e., the five Vs). This is a typical MCDM problem that is can be resolved by TOPSIS and, academically, TOPSIS represents the most commonly acceptable MCDM tool to handle such an issue, particularly, in the fields of SCM and the environment [ 119 ]. Note that both ANP and TOPSIS were carried out with the aid of the fuzzy set theory because the fuzzy-based ANP and TOPSIS reduce the effect of subjectivity [ 120 , 121 ]. Regarding RQ3, optimal resource mobilization cannot be attained unless the eight GSCM practices receive the entitled priority in terms of which practice should be implemented before the other one. Such a special configuration of a decision-making situation requires a tool that can handle the complexity amongst the investigated practices [ 106 ]. Hence, in this regard, ISM was found to be a better application because it has been widely validated as an effective decision-making method and, specifically, as a tool to handle the complexity issue [ 122 ]. Collectively, the sequence the three techniques are applied in creates a hybrid self-assessment model to measure the environmental maturity of the SCF (Figure 1). Logistics 2021, 5, x FOR PEER REVIEW 7 of 25 Figure 1. The perception of the proposed hybrid self-assessment model. 3.1. Fuzzy Analytic Network Process The ANP is the generalized form of the analytic hierarchy process (AHP), a wellknown MCDM technique [115]. Consequently, the AHP and fuzzy AHP can be considered special ANP and fuzzy ANP cases. Accordingly, the fuzzy ANP has been used as an extension of the traditional fuzzy AHP applications [123,124]. In AHP and fuzzy AHP, groups of elements are organized in a hierarchal structure. The application of the ANP and fuzzy ANP facilitates the formulation of more complex relationships through sets of clusters (network-based structure). These ideas have been explored in several studies [125–127]. In the ANP, a numerical scale is used to make sets of judgments, and in the fuzzy ANP, a linguistic scale is used. The MCDM literature provides several approaches to the implementation of the fuzzy AHP and ANP [128–136]. However, most of these approaches are complex. Hence, Chang’s extent analysis has been considered the most accepted method because of its simplicity [124,137,138]. The present study employed Chang’s extent analysis, which has been applied in previous studies [138,139]. To implement Chang’s extent analysis, let  symbolize the object set such that = 󰇝󰇘,󰇘,…,󰇘󰇞 and  symbolizes the goal set such that =󰇝,,…,󰇞. This application of Chang’s extent analysis implies that the objects are considered independently in a sequential approach. In addition, for each independent object (i.e., 󰇘,󰇘,…,󰇘), the analysis is executed for each goal, . Accordingly, numbers of extent analyses are executed as follows:   ,   ,…,   ,  =1,2,…, (1) where  ,(=1,2,…,) symbolizes the triangular fuzzy numbers. () symbolizes the membership function of the triangular fuzzy number. According to Erensal et al. [137], Chang’s extent analysis can be illustrated in four steps: Step 1: For each object , the fuzzy synthetic extent can be expressed as: =    ⨂󰇯      󰇰 (2) Figure 1. The perception of the proposed hybrid self-assessment model. Logistics 2021,5, 22 7 of 24 3.1. Fuzzy Analytic Network Process The ANP is the generalized form of the analytic hierarchy process (AHP), a wellknown MCDM technique [ 115 ]. Consequently, the AHP and fuzzy AHP can be considered special ANP and fuzzy ANP cases. Accordingly, the fuzzy ANP has been used as an extension of the traditional fuzzy AHP applications [ 123 , 124 ]. In AHP and fuzzy AHP, groups of elements are organized in a hierarchal structure. The application of the ANP and fuzzy ANP facilitates the formulation of more complex relationships through sets of clusters (network-based structure). These ideas have been explored in several studies [125–127]. In the ANP, a numerical scale is used to make sets of judgments, and in the fuzzy ANP, a linguistic scale is used. The MCDM literature provides several approaches to the implementation of the fuzzy AHP and ANP [ 128 – 136 ]. However, most of these approaches are complex. Hence, Chang’s extent analysis has been considered the most accepted method because of its simplicity [ 124 , 137 , 138 ]. The present study employed Chang’s extent analysis, which has been applied in previous studies [138,139]. To implement Chang’s extent analysis, let e Q symbolize the object set such that e Q=.. q1,.. q2, . . . , .. qm and e R symbolizes the goal set such that e R={r1,r2, . . . , rn} . This application of Chang’s extent analysis implies that the objects are considered independently in a sequential approach. In addition, for each independent object (i.e., .. q1 , .. q2 , . . . , .. qm ), the analysis is executed for each goal, rj . Accordingly, n numbers of extent analyses are executed as follows: e F1 rj,e F2 rj, . . . , e Fn rj,j=1, 2, . . . , m(1) where e Fi rj , (i=1, 2, . . . , n) symbolizes the triangular fuzzy numbers. e F(x) symbolizes the membership function of the triangular fuzzy number. According to Erensal et al. [ 137 ], Chang’s extent analysis can be illustrated in four steps: Step 1: For each object j, the fuzzy synthetic extent can be expressed as: Cj= n ∑ i=1e Fi rj ⊗"m ∑ j=1 n ∑ i=1e Fi rj#−1 (2) As expressed in Equation (2), ⊗ symbolizes the extended multiplication of two fuzzy numbers. To perform n ∑ i=1e Fi rj,nextent analysis values are executed such that: n ∑ i=1e Fi rj = n ∑ i=1 ki,n ∑ i=1 wi,n ∑ i=1 ti!(3) From Equation (2), h∑m j=1∑n i=1e Fi rji−1 can be obtained by performing the fuzzy addition operation of e Fi rj,(i=1, 2, . . . , n)as shown in Equation (4): m ∑ j=1 n ∑ i=1e Fi rj = m ∑ j=1 kj,m ∑ j=1 wj,m ∑ j=1 tj!(4) The inverse of the vector can then be obtained: "m ∑ j=1 n ∑ i=1e Fi rj#−1 = 1 ∑m j=1tj ,1 ∑m j=1wj ,1 ∑m j=1kj!(5) where pj,nj,hj> 0. After that, the Cjcan eventually be obtained such that: Logistics 2021,5, 22 8 of 24 Cj= n ∑ i=1e Fi rj ⊗"m ∑ j=1 n ∑ i=1e Fi rj#−1 = n ∑ i=1 ki⊗ m ∑ j=1 kj,n ∑ i=1 wi⊗ m ∑ j=1 wj,n ∑ i=1 ti⊗ m ∑ j=1 tj!(6) Step 2: The opportunity that [ f F2=(h2,n2,p2)]≥[e F1=(h1,n1,p1)] can be expressed as: V(e F2≥e F1V) =      1i f w2≥w1 0i f k1≥t2 k1−t2 (w2−t2)−(w1−k1)otherwise (7) Figure 2illustrates all the cases of Vf F2≥f F1 . For example, in the case of w2>k1 > t2 > w1 , point zrepresents the value that matches the highest intersection point of f F2and e F1 (which is point .. Z ). Both values, Vf F2≥f F1 and Vf F1≥f F2 , are required to compare f F2and e F1. Logistics 2021, 5, x FOR PEER REVIEW 8 of 25 As expressed in Equation (2), ⨂ symbolizes the extended multiplication of two fuzzy numbers. To perform ∑   ,  extent analysis values are executed such that:       =   ,   ,    (3) From Equation (2), ∑∑     can be obtained by performing the fuzzy addition operation of  ,(=1,2,…,) as shown in Equation (4):         =󰇭   ,   ,   󰇮 (4) The inverse of the vector can then be obtained: 󰇯        󰇰=󰇧 1 ∑   ,1 ∑   ,1 ∑   󰇨 (5) where , , ℎ > 0. After that, the  can eventually be obtained such that: =      ⨂󰇯        󰇰 =󰇭   ⨂   ,   ⨂   ,   ⨂   󰇮 (6) Step 2: The opportunity that [  =(ℎ,,)]≥[ =(ℎ,,)]can be expressed as:   ≥  =   1≥ 0≥ − (   −  ) − (   −  ) ℎ (7) Figure 2 illustrates all the cases of  ≥ . For example, in the case of  > >  > , point z represents the value that matches the highest intersection point of  and  (which is point 󰇘). Both values,  ≥  and  ≥ , are required to compare  and . Figure 2. All possible cases of  ≥ . Figure 2. All possible cases of Vf F2≥f F1. Step 3: The chance of the occurrence of a convex fuzzy number greater than y convex fuzzy numbers e Fj(j=1, 2, . . . , y)can be expressed as follows: Ve F≥e F1,e F2, . . . , e Fy=min Ve F≥e Fj,(j=1, 2, . . . , y)(8) Step 4: The last step is to find the weight vector for y=1, 2, . . . , msuch that: W=min V(C1≥Cy, min V(C2≥Cy), . . . , min V(Cy≥Cm))T(9) 3.2. Fuzzy TOPSIS The fuzzy TOPSIS was developed by Chen [ 140 ] to address the issue of uncertainty in MCDM problems. With the fuzzy TOPSIS, decision-makers, DMr , (r=1, . . . , k) use linguistic terms to rate the criteria and alternatives. Accordingly, e wr j represents the weight of the criterion j assigned by the DMr such that Cj , (j=1, . . . , m) . Correspondingly, e xr ij represents the weight of the alternative i with respect to Cj assigned by the DMr, such that Ai , (i=1, . . . , n) . Several studies [ 38 , 141 ] have summarized the fuzzy TOPSIS as follows: Logistics 2021,5, 22 15 of 24 In other words, the successful application of the GSCM-related efforts and resources are dependent on TOPM assurances and implementation. In contrast, SP&D is considered to result from the implementation of the other GSCM practices. Thus, the effort and resources needed for the implementation of SP&D cannot be technically justified. The application of the same logic and argument to all GSCM practices to improve the SCF environment (Figure 7) represents an effective initial GSCM implementation roadmap that considers the prioritized levels corresponding to each set of practices. Table 7. Computed driving power and dependence power for each green supply chain management practice. CUST SUP ENV QUAL TOPMC GHRM SP&D ORGM Driving Power CUST 1 1 1 1 0 0 1 0 5 SUP 1 1 1 1 0 0 1 0 5 ENV 1 1 1 1 0 0 1 0 5 QUAL 1 1 1 1 0 0 1 0 5 TOPMC 1 1 1 1 1 1 1 1 8 GHRM 1 1 1 1 0 1 1 1 7 SP&D 0 1 1 1 0 0 1 0 4 ORGM 1 1 1 1 0 1 1 1 7 Dependence Power 7 8 8 8 1 3 8 3 Logistics 2021, 5, x FOR PEER REVIEW 16 of 25 Table 7. Computed driving power and dependence power for each green supply chain management practice. CUST SUP ENV QUAL TOPMC GHRM SP&D ORGM Driving Power CUST 1 1 1 1 0 0 1 0 5 SUP 1 1 1 1 0 0 1 0 5 ENV 1 1 1 1 0 0 1 0 5 QUAL 1 1 1 1 0 0 1 0 5 TOPMC 1 1 1 1 1 1 1 1 8 GHRM 1 1 1 1 0 1 1 1 7 SP&D 0 1 1 1 0 0 1 0 4 ORGM 1 1 1 1 0 1 1 1 7 Dependence Power 7 8 8 8 1 3 8 3 Figure 5. Location of green supply chain management (GSCM) practices in four zones. Figure 5. Location of green supply chain management (GSCM) practices in four zones. Logistics 2021,5, 22 16 of 24 Logistics 2021, 5, x FOR PEER REVIEW 17 of 25 Figure 6. Identification of GSCM practice levels through four sophisticated iterations of interpretive structural modeling (ISM). Figure 7. Final diagraph of ISM. Figure 6. Identification of GSCM practice levels through four sophisticated iterations of interpretive structural modeling (ISM). Logistics 2021, 5, x FOR PEER REVIEW 17 of 25 Figure 6. Identification of GSCM practice levels through four sophisticated iterations of interpretive structural modeling (ISM). Figure 7. Final diagraph of ISM. Figure 7. Final diagraph of ISM. Logistics 2021,5, 22 17 of 24 5. Implications, Contributions, and Directions for Future Studies Synergized outcomes can be extracted when ISM results are interpreted by RRI scores or vice versa. In Figure 8, which is an adjustment of Figure 5, the size of the bubble is a reflection of the corresponding RRI score for each GSCM practice. It indicates that the drivers (independent practices) are less ready for facilitating SCF implementation than the linkage practices or the sole dependent practice—i.e., SP&D. For example, TOPM was considered as a driver. The corresponding RRI score was very low (50%). This indicates that TOPM lacked sufficient readiness or maturity to enhance SCF. Nevertheless, it was assumed to be the most ready practice because of the corresponding results for its driving power (8—i.e., high), dependence power (1—i.e., low), and phase or priority level ( Phase 1 ). In contrast, SP&D was considered a dependent practice. The corresponding RRI score was the highest (100%). This indicates that SP&D was the readiest practice. However, this was counter to its corresponding driving power (4), which was the lowest; dependence power (8) , which was the highest (i.e., = 8); and phase or priority level, which was the lowest (Phase 4). Logistics 2021, 5, x FOR PEER REVIEW 18 of 25 5. Implications, Contributions, and Directions for Future Studies Synergized outcomes can be extracted when ISM results are interpreted by RRI scores or vice versa. In Figure 8, which is an adjustment of Figure 5, the size of the bubble is a reflection of the corresponding RRI score for each GSCM practice. It indicates that the drivers (independent practices) are less ready for facilitating SCF implementation than the linkage practices or the sole dependent practice—i.e., SP&D. For example, TOPM was considered as a driver. The corresponding RRI score was very low (50%). This indicates that TOPM lacked sufficient readiness or maturity to enhance SCF. Nevertheless, it was assumed to be the most ready practice because of the corresponding results for its driving power (8—i.e., high), dependence power (1—i.e., low), and phase or priority level (Phase 1). In contrast, SP&D was considered a dependent practice. The corresponding RRI score was the highest (100%). This indicates that SP&D was the readiest practice. However, this was counter to its corresponding driving power (4), which was the lowest; dependence power (8), which was the highest (i.e., = 8); and phase or priority level, which was the lowest (Phase 4). Figure 8. Integration of Relative Readiness Index scores and categorized GSCM practices. To overcome this issue, the present study developed a strategic matrix tool (SMT), which is important for the reorganization and remobilization of the available resources. In Figure 9, the prioritized phases are listed vertically by the corresponding GSCM practice categories (zones). This also illustrates the horizontal classification of the RRI scores into three categories: 1, RRI score ≤ 50%; 2, RRI score ≥ 50% up to 90%; and 3, RRI score = 90% up to 100%. Therefore, three strategies were developed. The first (Strategy 1) refers to the process of increasing the allocated resources for each GSCM practice located in any square labeled “very high”, “high”, or “normal” attention in Phases 1 and 2 (Figure 9). The second (Strategy 2) refers to the decrease in the allocated resources for each GSCM practice located in any square labeled “very high”, “high”, or “normal” attention in Phases 3 or 4. The third (Strategy 3) is the maintenance of the same level of resources for each GSCM practice located in any square labeled “low” attention in Phases 1, 2, 3, or 4. Figure 8. Integration of Relative Readiness Index scores and categorized GSCM practices. To overcome this issue, the present study developed a strategic matrix tool (SMT), which is important for the reorganization and remobilization of the available resources. In Figure 9, the prioritized phases are listed vertically by the corresponding GSCM practice categories (zones). This also illustrates the horizontal classification of the RRI scores into three categories: 1, RRI score ≤ 50%; 2, RRI score ≥ 50% up to 90%; and 3, RRI score = 90% up to 100%. Therefore, three strategies were developed. The first (Strategy 1) refers to the process of increasing the allocated resources for each GSCM practice located in any square labeled “very high”, “high”, or “normal” attention in Phases 1 and 2 (Figure 9). The second (Strategy 2) refers to the decrease in the allocated resources for each GSCM practice located in any square labeled “very high”, “high”, or “normal” attention in Phases 3 or 4. The third (Strategy 3) is the maintenance of the same level of resources for each GSCM practice located in any square labeled “low” attention in Phases 1, 2, 3, or 4. The strategies for each practice are assigned on the basis of their location in the SMT (Figure 9). Logistics 2021,5, 22 18 of 24 Logistics 2021, 5, x FOR PEER REVIEW 19 of 25 The strategies for each practice are assigned on the basis of their location in the SMT (Figure 9). Figure 9. Proposed strategic matrix tool. This study contributes to the context of MCDM and its applications in GSCM from different angles. From a technical point of view, in order to evaluate the eight GSCM practices, fuzzy TOPSIS was conducted innovatively by using the relevant linguistic expression corresponding to to each V of big data (i.e., each criterion), as shown in Figure 4, which is a relatively more appropriate and accurate application compared to several traditional applications of fuzzy TOPSIS. Additionally, the study succeeded in visualizing the resources throughout a developed version of the ISM’s final structure as shown in Figure 8. Such an innovative representation facilitates the resource allocation adjustment process to ensure better utilization of resources by each GSCM practice. Specifically, note that the size of the bubble is a reflection of the dedicated resources for each GSCM practice, which is a developed and unique form of ISM results representation. Such technical contributions lead the talk to the practical/managerial contributions of the developed model. With such innovative tools proposed herein (i.e., BRI, RRI, and SMT), decision-makers can precisely allocate the required resources for the “demanding” GSCM practices with respect to the appropriate phase (i.e., timing). This can be performed by reducing the amount of resources dedicated to the GSCM practices in which resources are overutilized (i.e., saturated practices). Such strategic managerial actions can be executed with the aid of the three proposed strategies, as illustrated in the proposed SMT (Figure 9). By following such an approach, practitioners can allocate more resources confidently to the independent GSCM practices (drivers) such as TOPM, GHRM, and ORGM because the current dedicated resources for these practices are below their requirements as “drivers”. Similarly, resources can be deducted confidently from the dependent GSCM practices such as QUAL, SP&D, ENVI, CUST, and SUPP. Such MCDM- driven practical/managerial approaches for resource allocation and/or strategy creation are applicable in many fields including sustainability [154–157]. In this study, opinion-based measures for big data were employed when carrying out fuzzy ANP and fuzzy TOPSIS. However, the employment of a data-driven approach Figure 9. Proposed strategic matrix tool. This study contributes to the context of MCDM and its applications in GSCM from different angles. From a technical point of view, in order to evaluate the eight GSCM practices, fuzzy TOPSIS was conducted innovatively by using the relevant linguistic expression corresponding to to each V of big data (i.e., each criterion), as shown in Figure 4 , which is a relatively more appropriate and accurate application compared to several traditional applications of fuzzy TOPSIS. Additionally, the study succeeded in visualizing the resources throughout a developed version of the ISM’s final structure as shown in Figure 8. Such an innovative representation facilitates the resource allocation adjustment process to ensure better utilization of resources by each GSCM practice. Specifically, note that the size of the bubble is a reflection of the dedicated resources for each GSCM practice, which is a developed and unique form of ISM results representation. Such technical contributions lead the talk to the practical/managerial contributions of the developed model. With such innovative tools proposed herein (i.e., BRI, RRI, and SMT), decision-makers can precisely allocate the required resources for the “demanding” GSCM practices with respect to the appropriate phase (i.e., timing). This can be performed by reducing the amount of resources dedicated to the GSCM practices in which resources are overutilized (i.e., saturated practices). Such strategic managerial actions can be executed with the aid of the three proposed strategies, as illustrated in the proposed SMT (Figure 9). By following such an approach, practitioners can allocate more resources confidently to the independent GSCM practices (drivers) such as TOPM, GHRM, and ORGM because the current dedicated resources for these practices are below their requirements as “drivers”. Similarly, resources can be deducted confidently from the dependent GSCM practices such as QUAL, SP&D, ENVI, CUST, and SUPP. Such MCDM-driven practical/managerial approaches for resource allocation and/or strategy creation are applicable in many fields including sustainability [154–157]. In this study, opinion-based measures for big data were employed when carrying out fuzzy ANP and fuzzy TOPSIS. However, the employment of a data-driven approach would provide more accurate interpretations. To illustrate this, instead of using linguistic terms in measuring the GSCM practices with respect to the five Vs (Figure 4), the volume of data, for example, can be measured in Terabytes (TB) or even Petabytes (PB), the velocity of the data Logistics 2021,5, 22 19 of 24 can also be measured, for example, in TB per second (TB/s), and so. Another direction for future research can be investigated within the context of resource utilization. Specifically, the required resources for each GSCM practice can be cascaded into a further levels of details such as the types of resources to be utilized (human, technical, organizational, physical, or financial), as discussed and illustrated in [118]. 6. Conclusions This study proposes a hybrid self-assessment model to measure the maturity of SCF considering the environmental dimension by the coherent application of three MCDM techniques: fuzzy ANP, fuzzy TOPSIS, and ISM. The five Vs of big data were found to provide a template for assessing of GSCM practices. The fuzzy ANP illustrates the quantification of the five Vs by considering their inner- and outer-dependence. The fuzzy TOPSIS measures the contribution of each GSCM practice to supply chain financial performance. Moreover, rather than extracting the final GSCM practice scores, as is typical of traditional fuzzy TOPSIS applications, the study developed two indices: the BRI and RRI. The BRI scores indicated that the GSCM practices in the investigated company were not mature enough to improve the implementation of SCF. However, the proposed RRI index facilitated the benchmarking of GSCM practices to create the initial improvement guidelines. Consequently, the application of ISM creates a roadmap for the improvement of each practice through the categorization by driver, linkage, and dependent practices. ISM also allows for additional interpretations through the computation of the driving and dependence power for each practice and the identification of the corresponding level for each set of practices. In sum, the proposed SMT creates the potential for further improvements through the integration of the RRI scores and ISM results. Hence, the proposed hybrid model offers a practical tool for decision-makers. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. 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